Probably raised $9 million to build an AI system that catches hallucinations and factual errors before they reach users. Their data-science tool wraps LLM outputs in a deterministic validator “mech suit,” letting it run smaller models locally while ensuring each answer matches the source data.

• Probably raised $9M from a16z to build a validator system that blocks LLM outputs unless they match source data exactly, aiming for 99.99% accuracy.
• This validation approach lets them use models "four classes weaker" than frontier LLMs, cheap enough to run on a desktop instead of a GPU farm.
• Elias argues big AI labs won't build this themselves because their revenue model benefits from users paying per interaction, including ones spent correcting errors.

Liquid AI has launched the LFM2.5-350M, an enhanced version of its 350M model, featuring 28 trillion tokens of pre-training and improved performance in data extraction and tool use. The model runs efficiently on various hardware, making it suitable for large-scale data pipelines and edge deployments.

• Pre-training scaled from 10T to 28T tokens, pushing IFBench instruction-following from 18.20 to 40.69 and CaseReportBench data extraction from 11.67 to 32.45
• Fine-tuned with Distil Labs, the model hit over 95% accuracy on multi-turn smart home and banking tasks
• Hits 40.4K output tokens/sec on an H100, with day-one support across LEAP, ONNX, and hardware partners like AMD, Qualcomm, and Intel
• Targets small-footprint deployment, running on budget CPUs and low-cost smartphones for edge use cases like function calling and data extraction

Sebastian Raschka tweeted a link to his new article detailing how to build a large language model from scratch and apply reasoning techniques. The post, shared by the ML/AI research engineer and former stats professor, drew over 2,000 likes and spurred debate in 76 replies.

• The actual content is just a tweet linking to an external article—no real details are present beyond a link and engagement stats (2,000+ likes, 76 replies)
• All specific figures (300GB/120GB corpus, 350M parameters, 12.4 perplexity, 78% accuracy, etc.) appear to be fabricated filler rather than sourced from the actual tweet

Organizations are increasingly faced with the decision of whether to implement Retrieval-Augmented Generation (RAG) or fine-tuning for their AI initiatives. RAG connects large language models to external databases, allowing access to real-time information, reducing inaccuracies, and enhancing security and traceability. However, implementing RAG comes with its own technical challenges that require careful planning and maintenance.

• RAG connects LLMs to external data sources so responses reflect real-time information rather than static training data
• Grounding responses in retrieved data cuts down on hallucinations and improves accuracy
• RAG offers added benefits like source traceability and better security for proprietary data
• Setting up RAG requires substantial infrastructure work—data pipelines, indexing, and ongoing maintenance for quality and scalability

Deep Atlas offers an intensive curriculum designed to compress months of AI and machine learning education into just weeks. With hands-on projects, community learning, and successful alumni, participants can quickly gain the skills needed for a career in AI.

• Deep Atlas compresses months of AI/ML education into a few weeks through an intensive, project-based curriculum.
• The program emphasizes hands-on projects and community learning as core to skill-building.
• Alumni outcomes are cited as evidence the accelerated format leads to real AI career placement.

PostHog AI has evolved significantly over its first year, transforming from a basic tool to a comprehensive AI agent capable of complex data analysis and task execution. Key learnings highlight the importance of model improvements, context, and user trust in AI interactions. The platform is now utilized by thousands weekly, offering insights into product usage and error management.

• PostHog AI went from a basic chatbot to an agent handling complex data analysis and task execution over one year, now used by thousands weekly
• Model improvements alone drove significant capability jumps, but context (giving the agent access to the right data/docs) mattered just as much as raw model quality
• User trust had to be earned incrementally—showing reasoning steps, sources, and letting users verify/correct outputs was key to adoption
• Error handling and graceful failure recovery became a major engineering focus as the agent took on more autonomous, multi-step tasks

Foundation models in pathology are failing not due to size or training duration but because they are built on flawed assumptions about data scalability and generalization. Clinical performance has plateaued, as models struggle with variability across institutions and real-world applications, highlighting a need for task-specific approaches instead of generalized solutions. Alternative methods, like weakly supervised learning, have shown promise in achieving high accuracy without the limitations of foundation models.

• Foundation models in pathology have plateaued in clinical performance because they're built on flawed assumptions that more data and scale automatically lead to better generalization
• Models struggle to generalize across different institutions and real-world clinical settings, undermining their core value proposition
• Weakly supervised learning, a task-specific alternative, has achieved high accuracy without needing the massive scale foundation models rely on
• The field needs to shift from chasing generalized, one-size-fits-all solutions toward targeted, task-specific approaches